(ii) \(\text{Temporal Convolution Block}\) : In recent literature (such as [1]}), the authors represented TCs for the sequence modeling tasks and showed that temporal convolutions could outperform recurrent networks such as LSTMs in a wide range of datasets and time-series tasks. More specifically, TCs offer several advantages over recurrent networks such as processing the input sequence as a whole rather than sequential training, low memory requirements, stable gradient, and capturing the past information with flexible receptive field size. Inspired by the performance of TCs for the sequential data, we used “\(\text{Temporal Convolution Block}\) ” (represented via an orange box in Fig. REF ) instead of recurrent networks for the sEMG-based HGR. As shown in Fig. REF , each \(\text{Temporal Convolution Block}\)  consists of dilated causal convolutions, where the dilation rate \(R\)  increases exponentially, i.e., (\(\mathrm {1, 2, 4, 8, \ldots }\) ), to access a receptive field that exceeds the length of the input sequence. Moreover, each dilated causal convolution is followed by a ReLU activation function. The number of \(\text{Temporal Convolution Block}\) s are based on the logarithmic scale with the number of patches \(N\) . More specifically, we used \(Z=\left\lceil {\log _2 N}\right\rceil \)  number of \(\text{Temporal Convolution Block}\)  for an input segment \(\mathbf {X}\)  with a sequence length of \(L\) .
